Defense and military — the primes, the suppliers, and the sector ETFs.
Bulk is a member of the Blaque Baux family. The core repo
is the engine and blueprint. Bulk points that engine at defense, aerospace, and
military-adjacent names and ETFs — ITA, XAR, PPA, DFEN, and primes like LMT, RTX,
NOC, GD, LHX. It inherits the engine's governance wholesale.
Not investment advice. Educational/research software. A single-sector book is concentrated by construction and exposed to policy/budget risk. Nothing here is validated. See LICENSE.
git clone --recursive https://github.com/blaquebaux/bulk.git
julia --project=engine -e 'using Pkg; Pkg.instantiate()' # one-time engine setupDefense has two features the family already knows how to test. First, it is event-driven: conflict and budget headlines move the whole sector at once — the tradeable question (as with Blunt's crude→refiner) is whether there is next-day follow-through or whether it is priced instantly. Second, it is a concentrated, correlated basket — Boom's crowding lesson applies: the primes move largely as one factor, so "diversification" across five defense names is mostly an illusion, and sizing must reflect that.
- Sector trend / momentum — ITA/XAR vol-targeted; the honest baseline for a defense tilt.
- Event follow-through — does a conflict/budget shock give next-day drift in the sector, or is it priced same-day (the Blunt/correlation test, applied to defense)?
- Crowding check — effective number of bets across the primes (Boom's participation-ratio method); size to the real factor count, not the name count.
- Backlog / earnings — order-backlog and earnings-reaction drift (needs a fundamentals feed).
Full detail in research/README.md. The scorecard:
| # | Question | Verdict |
|---|---|---|
| 1 | Where on the Basel↔Bio correlation spectrum? | ✅ moderate factor — corr 0.52, 58% one-factor, ~2.7 bets/8 |
| 2a | Do geopolitical shocks give next-day drift? | ❌ priced instantly (corr +0.00) |
| 2b | Does trend+vol-target help? | ❌ no — defense whipsaws (+0.75→+0.44) |
| 2c | Is defense a risk-off hedge? | ❌ no — high-beta industrials (β 0.96, falls as much on worst days) |
The synthesis: Bulk is a null for a systematic sleeve, with one useful diagnostic. Defense fills the middle of the family's correlation spectrum — Bio 36% → Bulk 58% → Basel 81% one-factor share — a moderate factor bound by a shared demand driver (defense budget/geopolitics) but kept distinct by program mix (~3 real bets, not 8). But none of the tradeable angles survive: geopolitical shocks are priced instantly (no drift, unlike crude→refiner), trend-following whipsaws, and defense is high-beta industrials (corr 0.74 / β 0.96 to SPY) that falls just as hard on the worst days — not a hedge. A fine buy&hold sector, no systematic alpha, no diversification.
Research: first pass complete — null (diagnostic only) (research/). No systematic edge;
defense is equity beta with a narrative. No live driver. Nothing validated to the spine's bar.
Blaque Baux is a quantitative research initiative and a subsidiary of Carter Warrens. BlaqueBaux.com is the home for the work; the code lives here on GitHub — open to study, test, and build bespoke strategies on top of.
Anyone can point an AI at a market. The edge is understanding what the data actually says — and turning it into something you can act on. We test relentlessly and put most of it on the record as rejected, with the reason; what survives is built, governed, and validated before it is ever called real. That combination — honest research, reproducible evidence, and execution you can trust — is why Carter Warrens leads on strategy and implementation, not merely uses the tools everyone now has.
This repo is one sleeve of the Blaque Baux family — a single governed engine steered in many directions. The core repo is the base/blueprint and holds the full family roster.
engine/ the Blaque Baux platform (git submodule → blaquebaux/base)
research/ two Path-A sketches (correlation spectrum, tradeability null) + scorecard
live/ governed live drivers (once a sleeve graduates to paper A/B)
MIT. © 2026 Carter Warrens.